• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.423-432
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• 2003

It is essential suity management and standardized asset analysis for telecommunication networks, however existing risk analysis methods and tools are not enough to give shape of the method to evaluate value and asset. they only support asset classification schemes. Moreover, since the existing asset classification schemes are to evaluate comprehensive general risk, they are not appropriate for being applied telecommunication networks and they can´t offer any solutions to an evaluator´s subjectivity problem. In this paper, to solve these problems, we introduce the standardized definition of asset evaluation model new asset classification scheme, two-dimensional asset process classification scheme to consider business process and asset, various evaluation standards for quantitative value and qualitative evaluation. To settle an valuator´s subjectivity problem, we proposed $\beta$-distribution Delphi method.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.309-326
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• 2008

NTIS(National Science & Technology Information Service) provides the information for domestic research projects. It in particular has several classification schemes to classify research projects and provide better retrieval and analysis services. It however is difficult to understand the characteristic of a research project clearly since only a classification in a classification scheme can be chosen about a research project. Moreover, the classification scheme covers the high-level classification for every research areas so that it cannot cover the area specialized to the oriental medicines. On the other hand, the classification schemes for oriental medicines have recently been studied in oriental medicine field. However, it also covers the high-level classification for oriental medicine so that it may not suit to a classification scheme for research projects. Therefore, in this paper we propose a classification scheme to understand clearly the characteristic of research projects in oriental medicine and use to use them to retrieval and analysis services.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.9-16
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• 2006

RDA(Relational Discriminant Analysis) is a way of finding classifiers based on the dissimilarity measures among the prototypes extracted from feature vectors instead of the feature vectors themselves. Therefore, the accuracy of the RDA classifier is dependent on the methods of selecting prototypes and measuring proximities. In this paper we propose to utilize PRS(Prototype Reduction Schemes) and Mahalanobis distances to devise a method of increasing classification accuracies. Our experimental results demonstrate that the proposed mechanism increases the classification accuracy compared with the conventional approaches for samples involving real-life data sets as well as artificial data sets.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.165-178
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• 2005

A product classification scheme is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eCl@ss, however, have a lot of limitations to meet these requirements for dynamic features of classification. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this Paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes, and describe the semantic classification model proposed in [1], which satisfies the requirements for dynamic features of product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.33-59
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• 2016

This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.3
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• pp.29-51
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• 2013

The purpose of this study is to develop internal tables in Christian religion in the 5th edition of Korean Decimal Classification. The scope of the Christianity, its structure of various classification schemes, and the concepts of internal tables were analyzed. The contents of several textbooks were analyzed for the scope of the discipline and the classification schemes and internal tables of DDC, UDC, NDC, LCC, Classification of the Library of Union Theological Seminary and the Classification of the Korea Theological Library were compared. And then, internal tables in Bible, sermon, worship, church history were built and those tables were evaluated by librarians and experts in the fields. And finally, internal tables of the Christiainity and new headings were suggested. New internal tables in Christianity will increase the effectiveness of information retrieval and it will provide a foundation for developing internal tables in other disciplines.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• Journal of Korean Library and Information Science Society
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• v.28
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• pp.193-218
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• 1998

The purpose of this study is to analyze and compare of the structure of auxiliary schedules using in DDC, UDC, CC and BC. Auxiliary schedule whish are a n.0, ppended to schedule of all schemes of classification. They consist of items of form of presentation relationship, time, place, languages, racial, ethnic, national groups and persons and phase relation and the symbols of the different items can be added to classification numbers.